Citation

Aziz O, Musngi M, Park EJ, Mori G, Robinovitch SN. Med. Biol. Eng. Comput. 2016; 55(1): 45-55.

Affiliation

School of Engineering Science, Simon Fraser University, Burnaby, BC, Canada.

Copyright

(Copyright © 2016, International Federation for Medical and Biological Engineering, Publisher Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/s11517-016-1504-y

PMID

27106749

Abstract

Falls are the leading cause of injury-related morbidity and mortality among older adults. Over 90 % of hip and wrist fractures and 60 % of traumatic brain injuries in older adults are due to falls. Another serious consequence of falls among older adults is the 'long lie' experienced by individuals who are unable to get up and remain on the ground for an extended period of time after a fall. Considerable research has been conducted over the past decade on the design of wearable sensor systems that can automatically detect falls and send an alert to care providers to reduce the frequency and severity of long lies. While most systems described to date incorporate threshold-based algorithms, machine learning algorithms may offer increased accuracy in detecting falls. In the current study, we compared the accuracy of these two approaches in detecting falls by conducting a comprehensive set of falling experiments with 10 young participants. Participants wore waist-mounted tri-axial accelerometers and simulated the most common causes of falls observed in older adults, along with near-falls and activities of daily living. The overall performance of five machine learning algorithms was greater than the performance of five threshold-based algorithms described in the literature, with support vector machines providing the highest combination of sensitivity and specificity.

Language: en